Phase correction device, action identification device, action identification system, microcontroller, phase correction method, and program

ABSTRACT

There are included a standard deviation calculation unit that receives a plurality of acceleration data and calculates a standard deviation of the plurality of acceleration data for each specified time period, an average calculation unit that receives the plurality of acceleration data and calculates an average value of the acceleration data for each specified time period, a phase estimation unit that estimates a phase of the average value in a space having a first coordinate axis and a second coordinate axis by using the average value when the standard deviation is smaller than a specified threshold, and a phase correction unit that performs phase correction of the average value by using the estimated phase.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese patent applications No. 2014-232807 filed on Nov. 17, 2014, No.2014-264297 filed on Dec. 26, 2014, and No. 2015-154753 filed on Aug. 5,2015, the disclosure of which is incorporated herein in its entirety byreference.

BACKGROUND

The present invention relates to a measurement device, a phasecorrection device, an action identification device, an actionidentification system, a microcontroller, a phase correction method, anda program.

Various devices that measure the amount of activity of people using anacceleration sensor and systems that identify the action of people basedon the measurement results have been proposed. Further, such devices andsystems are applied also to measurement of the amount of activity andidentification of the action of animals other than people.

Japanese Unexamined Patent Application Publication No. 2011-217928discloses a system that mounts an acceleration sensor to an animal,converts acceleration data acquired from the acceleration sensor into anangle, and estimates the state of the animal from the angle and kineticmomentum. According to this technique, the acceleration sensor thatdetects the accelerations in three axes (x-axis, y-axis and z-axis) thatare orthogonal to one another is fixed to a harness that is worn aroundan animal's body or a collar. When fixing the acceleration sensor, thex-axis, the y-axis and the z-axis are set to coincide with thefront-back direction, the left-right direction and the up-down directionof an animal, respectively. Then, a measurement device calculates afront-back tilt angle θx and a left-right tilt angle θy of the x-y planewith respect to the z-direction based on the acceleration data detectedby the acceleration sensor and displays a change in those tilt anglestwo-dimensionally. The measurement device further performs short-timeFourier transform on synthetic acceleration obtained by synthesizing thethree-axis acceleration data. It is thereby possible to decompose thesynthetic acceleration into frequency components and calculate afrequency distribution. The measurement device identifies the action ofthe animal based on the frequency distribution.

Japanese Unexamined Patent Application Publication No. H10-267651discloses a method for correcting the displacement between the axis ofan acceleration sensor mounted on an object under test and the axis ofthe object under test when they do not match. Specifically, a regularhexahedron housing on which an acceleration sensor is mounted is broughtinto contact with an object under test to measure the acceleration ofgravity in the state of rest. This procedure is performed on every sideof the hexahedron.

The action identification by an acceleration sensor is applied also topeople. Japanese Unexamined Patent Application Publication No.2013-094316 discloses a method for estimating the walking state ofpeople in a short time. According to this technique, an accelerationsensor is worn near the waist of a target person to detect theacceleration in the horizontal direction (left-right direction) that issubstantially orthogonal to the moving direction of the person. Then,the variation of autocorrelation of acceleration data during walking isused as a feature quantity, and the walking state is estimated using SVM(Support Vector Machine).

SUMMARY

However, according to the description of the configuration disclosed inJapanese Unexamined Patent Application Publication No. 2011-217928, theacceleration sensor is mounted on an animal with the axis of theacceleration sensor and the gravity direction coinciding with eachother. However, it is difficult to accurately achieve this.Specifically, when an animal moves, the position on which theacceleration sensor is mounted and the axial direction change. Then, thestandard value of the acceleration differs in each measurement, whichmakes it difficult to accurately estimate the state of the animal fromthe acceleration data.

The configuration disclosed in Japanese Unexamined Patent ApplicationPublication No. H10-267651 has a problem that it is difficult to producethe resting state for calibration with the acceleration sensor mountedon an animal. Further, because it discloses a method for correcting thedisplacement between axes in the early stage when the accelerationsensor is mounted on an object under test, when the axial directionvaries with time such as when the acceleration sensor is mounted on ananimal, the acceleration data is acquired without correcting thedisplacement between axes. Then, in the case of correcting thedisplacement occurring between axes as needed, the act of manuallycontrolling the acceleration sensor or the like is required, such asmeasuring the acceleration several times as changing the position of theside of the hexahedron of the housing.

Further, both of Japanese Unexamined Patent Application PublicationsNos. 2011-217928 and 2013-094316 perform the action identification basedon the assumption that the acceleration sensor is placed in a desiredposition and direction, and the position and direction of theacceleration sensor that have been determined once are maintained.However, in reality, even for the same object, it is extremely difficultto always place the acceleration sensor in a fixed position anddirection. If the placement position and direction of the accelerationsensor are different each time, phase displacement occurs in theacceleration data obtained each time.

Further, there is a case where the acceleration sensor is graduallydisplaced from the initial placement position and direction due to themotion of an object. In this case also, phase displacement occurs in theacceleration data with the lapse of time.

The action identification using an acceleration sensor requires aprocedure to determine a threshold and machine learning parameter basedon acceleration data. If the displacement of the placement position anddirection of the acceleration sensor does not occur, the same thresholdor parameter can be applied every time to the same object no matter howmany times the placement of the acceleration sensor and the acquisitionof the acceleration data are performed. However, as described above, inthe case where a phase difference in acceleration data occurs in eachmeasurement, it is necessary to calculate a threshold and parameter foreach measurement. In other words, it is difficult to use a commonthreshold and parameter for a plurality of measurements. Further, ittakes a lot of time and work to calculate a threshold and parameter foreach measurement, which is inefficient.

One solution to the above problem is to perform calibration when placingan acceleration sensor at an object and thereby correct a phasedifference. However, it is necessary to maintain the resting stateduring calibration. If the object is an animal, it is difficult tomaintain the resting state for a certain period of time. Further, thismethod cannot correct a phase difference that occurs due to the motionof an object after placement or the like.

The present invention has been accomplished to solve the above problemsand an object of the present invention is thus to provide a phasecorrection device, an action identification device, an actionidentification system, a microcontroller, a phase correction method anda program capable of making appropriate correction.

The other problems and novel features of the present invention willbecome apparent from the description of the specification and theaccompanying drawings.

According to one embodiment, a phase correction device includes astandard deviation calculation unit that receives a plurality ofacceleration data and calculates a standard deviation of the pluralityof acceleration data for each specified time period, a representativevalue calculation unit that receives the plurality of acceleration dataand calculates a representative value of the acceleration data for eachspecified time period, a phase estimation unit that estimates a phase ofthe representative value in a space having a first coordinate axis and asecond coordinate axis by using the representative value when thestandard deviation is smaller than a specified threshold, and a phasecorrection unit that performs phase correction of the representativevalue by using the estimated phase.

According to one embodiment, an action identification device includesthe above-described phase correction device, an identification learningunit that performs machine learning by using the representative valueafter phase correction by the phase correction device, and anidentification processing unit that performs action identification byusing the representative value after phase correction by the phasecorrection device.

According to one embodiment, an action identification system includesthe above-described action identification device, and a transmittingunit including an acceleration sensor that is mounted on an object andoutputs the acceleration data.

According to one embodiment, a microcontroller includes theabove-described action identification device, and a resistor settingunit that sets any one of the identification learning unit and theidentification processing unit to an operating state according toexternal control.

According to one embodiment, a phase correction method includes astandard deviation calculation step of receiving a plurality ofacceleration data and calculating a standard deviation of the pluralityof acceleration data for each specified time period, a representativevalue calculation step of receiving the plurality of acceleration dataand calculating a representative value of the acceleration data for eachspecified time period, a phase estimation step of estimating a phase ofthe representative value in a space having a first coordinate axis and asecond coordinate axis by using the representative value when thestandard deviation is smaller than a specified threshold, and a phasecorrection step of performing phase correction of the representativevalue by using the estimated phase.

One embodiment is a program for causing a computer to execute the phasecorrection described above.

According to the present invention, it is possible to provide ameasurement device, a measurement system, a measurement method and aprogram capable of correcting a mounting error of an acceleration sensorby using statistical information of acceleration data even after theacceleration sensor is mounted on an object under test.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, advantages and features will be moreapparent from the following description of certain embodiments taken inconjunction with the accompanying drawings, in which:

FIG. 1 is a view showing the configuration of a measurement system 100according to a first embodiment.

FIG. 2 is a view showing the concept of correction processing accordingto the first embodiment.

FIG. 3 is a flowchart showing the operation of the measurement system100 according to the first embodiment.

FIG. 4 is a flowchart showing the operation of a measurement system 100according to a second embodiment.

FIG. 5 is a view showing the operation of the measurement system 100according to the second embodiment.

FIG. 6 is a view showing the concept of correction processing accordingto a third embodiment.

FIG. 7 is a flowchart showing the operation of a measurement system 100according to the third embodiment.

FIG. 8 is a flowchart showing the operation of a measurement system 100according to a fourth embodiment.

FIG. 9 is a flowchart showing the operation of a measurement system 100according to a fifth embodiment.

FIG. 10 is a view showing the concept of correction processing accordingto the fifth embodiment.

FIG. 11 is a view showing the concept of correction processing accordingto the fifth embodiment.

FIG. 12 is a view showing the concept of correction processing accordingto a sixth embodiment.

FIG. 13 is a flowchart showing the operation of a measurement system 100according to the sixth embodiment.

FIG. 14 is a view showing the configuration of a measurement system 100according to a seventh embodiment.

FIG. 15 is a list of variables used in the description of embodiments ofthe present invention.

FIG. 16 is a view showing the configuration of a phase correction device150 according to an eighth embodiment.

FIG. 17 is a view showing the operation of the phase correction device150 according to the eighth embodiment.

FIG. 18 is a view showing the overview of phase correction processingaccording to the eighth embodiment.

FIG. 19 is a view showing the overview of phase correction processingaccording to the eighth embodiment.

FIG. 20 is a view showing the overview of phase correction processingaccording to a ninth embodiment.

FIG. 21 is a view showing the overview of phase correction processingaccording to the ninth embodiment.

FIG. 22 is a view showing the configuration of an action identificationdevice 200 according to the ninth embodiment.

FIG. 23 is a view showing the operation of the action identificationdevice 200 according to the ninth embodiment.

FIG. 24 is a view showing the overview of action identificationprocessing according to a tenth embodiment.

FIG. 25 is a view showing the configuration of an action identificationsystem 300 according to an eleventh embodiment.

FIG. 26 is a view showing the configuration of a microcontroller 400according to a twelfth embodiment.

DETAILED DESCRIPTION

Specific embodiments of the present invention are described hereinafterin detail with reference to the drawings.

First Embodiment

The configuration of a measurement system 100 according to a firstembodiment of the present invention is described first with reference toFIG. 1.

The measurement system 100 includes a measurement device 110 and asensor module 120.

The sensor module 120 is used by being mounted on an object under test,and measures the acceleration generated by the motion of the objectunder test and outputs acceleration data indicating a measured value.The sensor module 120 typically includes an acceleration sensor, an MCU(Micro Control Unit) that generates acceleration data from an outputsignal of the acceleration sensor and outputs it, and an RF unit thatmodulates the acceleration data and transmits it by wireless way.Instead of the RF unit, the sensor module 120 may include variouscommunication interfaces for transmitting acceleration data by wiredway. It is assumed that the acceleration sensor is capable of outputtingthe three-axis (x, y and z) acceleration.

The measurement device 110 receives the acceleration data output fromthe sensor module 120, performs statistical processing on theacceleration data and thereby modifies a mounting error of theacceleration sensor. The measurement device 110 is typically aninformation processing device such as a PC (personal computer) or aserver computer, and it is implemented by a CPU (Central ProcessingUnit), a storage device such as a volatile or nonvolatile memory, aninput/output device and the like. The measurement device 110 performsspecified processing based on a program stored in the storage device andthereby logically implements an input unit 111 and an analysis unit 112,which are described later.

The input unit 111 receives the acceleration data from the sensor module120. The acceleration data is received by reception and demodulation ofa radio signal using an access point or by wired communication usingcommunication interfaces, for example.

The analysis unit 112 performs statistical processing on theacceleration data acquired by the input unit 111 and modifies a mountingerror of the acceleration sensor.

The overview of the way of modifying a mounting error of theacceleration sensor by using the measurement system 100 according to thefirst embodiment is described next. The case where an object under testis an animal and the sensor module 120 is mounted on the animal toacquire acceleration data is described hereinafter as an example.

First, the sensor module 120 is mounted on an animal. The sensor module120 is preferably mounted on a position in the axial direction whereacceleration data can be easily translated later and at which the motionor acceleration is likely to occur. In this embodiment, the sensormodule 120 is mounted on an animal's head. Further, the sensor module120 is mounted so that, among the three axes (x, y and z) which thesensor module 120 can measure, the x-direction coincides with the movingdirection of the animal, the y-direction coincides with the left-rightdirection of the animal, and the z-direction coincides with the up-downdirection of the animal.

Next, acceleration data is acquired by letting the animal act freely fora given period of time such as 10 minutes, for example. The sensormodule 120 transmits the acceleration data generated by the accelerationsensor by wireless or wired to the measurement device 110.

The measurement device 110 receives the acceleration data and performsthe following processing. First, the analysis unit 112 of themeasurement device 110 calculates the average value of the accelerationdata for each certain time period. For example, in the case of acquiringthe acceleration data at a sampling rate of 400 Hz and calculating theaverage value every one second, the average value of 400 pieces ofacceleration data during one second is obtained. This average value isreferred to hereinafter as the average acceleration. The analysis unit112 performs the calculation of the average acceleration for each of thethree directions (x, y and z).

Then, the analysis unit 112 compares the statistical distribution ofactually acquired acceleration data and the statistical distribution ofpredetermined ideal acceleration data and corrects the actually acquiredacceleration data. The concept of this processing is describedhereinbelow.

FIG. 2 is a graph in which the average accelerations in the x-directionand the y-direction calculated from the actually acquired accelerationdata are plotted with circle marks “o”. On the other hand, plus marks“+” indicate the predetermined ideal average acceleration in thex-direction and the y-direction.

Generally, when the average accelerations are plotted, the distributionwith a certain pattern is obtained according to the posture or action ofan animal. In this example, because the acceleration sensor is mountedso that the y-direction coincides with the left-right direction of ananimal, ideally, the average accelerations are distributed along theline a_(Y)=0 of acceleration in the y-direction according to thesymmetrical motion of the animal (“+” distribution). In other words, thedistribution of ideal average accelerations should approximate to theline a_(Y)=0.

However, in reality, the left-right direction of an animal and they-axis do not coincide due to a mounting error of the accelerationsensor. Further, due to a reason such as a change in the mountingposition caused by the motion of an animal, the ideal axis predeterminedand the actual axis of the acceleration sensor are deviated from eachother. Accordingly, the distribution of actual average accelerations(the distribution of “o”) is deviated from the distribution of idealaverage accelerations (the distribution of “+”). Specifically, theapproximation line of the distribution of actual average accelerationsis slightly tilted relative to the approximation line of thedistribution of ideal average accelerations.

The analysis unit 112 calculates the slope of the approximation line ofaverage accelerations in the x-direction and the y-direction by theleast squares method, for example. At this time, a data region to beread may be specified in order to improve the accuracy. The calculatedslope indicates the displacement between the ideal axis and the actualaxis. For example, the analysis unit 112 corrects the acceleration databy a correction coefficient that is calculated based on the calculatedslope.

Note that, although the correction coefficient is calculated based onthe statistical distribution of average accelerations in the twodirections x and y in this embodiment, the distribution in another givenaxis direction may be used if the ideal statistical distribution can bepredetermined. Further, it is not limited to the two directions, and thecorrection coefficient may be calculated in the same manner using thestatistical distribution in the three-dimensional space where the threeaxes x, y and z are plotted.

Further, in the above-described process, it is not always necessary forthe analysis unit 112 to generate a graph where the distribution ofactual average accelerations (the distribution of “o”) and thedistribution of ideal average accelerations (the distribution of “+”)are plotted in practice. The analysis unit 112 needs to perform only thecalculation of a correction coefficient based on the displacement of thestatistical distribution on the background of the concept as describedabove. However, it is preferred that the analysis unit 112 carries outthe generation and display of the above-described graph in order topresent it to a user, for example.

Specific processing for the correction of acceleration data describedabove which is performed by the analysis unit 112 is describedhereinafter in detail with reference to the flowchart of FIG. 3.

S101: Acquire Acceleration Data

The input unit 111 of the measurement device 110 receives accelerationdata that is transmitted from the sensor module 120 by wireless or wiredcommunication. The input unit 111 can end the acquisition ofacceleration data when a predetermined measurement time has passed.

S102: Calculate Average Acceleration Per Unit Time

The analysis unit 112 calculates the average value of accelerations foreach certain time period based on the acceleration data that is acquiredby the input unit 111. For example, in the case of calculating theaverage value every one second, the analysis unit 112 divides the wholeacceleration data into groups of every one second and calculates theaverage value for each group. The analysis unit 112 performs the sameprocessing for all of the three directions (x, y and z) where theacceleration sensor outputs the acceleration data.

S103: Calculate Slope of Approximation Line of Distribution of AverageAccelerations

The analysis unit 112 calculates a slope A of a straight line thatapproximates to the average accelerations calculated in S102. Forexample, the slope A of the approximation line on the x-y plane can becalculated by the following equation (1)

$\begin{matrix}{{Equation}\mspace{14mu} 1} & \; \\{A = \frac{{\Sigma_{i}\left( {{a_{X}(i)} - {M\left( a_{X} \right)}} \right)}\left( {{a_{Y}(i)} - {M\left( a_{Y} \right)}} \right)}{{\Sigma_{i}\left( {{a_{X}(i)} - {M\left( a_{X} \right)}} \right)}^{2}}} & (1)\end{matrix}$

In the above equation, a_(X)(i) is the value of the x-component of thei-th average acceleration, M(a_(X)) is the average value of x-componentsof all average accelerations, a_(Y)(i) is the value of the y-componentof the i-th average acceleration, and M(a_(Y)) is the average value ofy-components of all average acceleration.

Note that the analysis unit 112 may perform the linear approximation inS103 by using only some of average accelerations, not all of averageaccelerations calculated in S102. For example, a data valid region thatexcludes average accelerations that are distributed near the circulararc with a radius of 1 shown in FIG. 2 is set, and the above-describedlinear approximation is performed using only the average accelerationsinside this data valid region. It is thereby possible to perform linearapproximation efficiently. Such a data valid region can be defined by acondition expression such as x²+y²<(0.8)², for example.

S104: Calculate and Correct Correction Coefficient

The analysis unit 112 calculates a correction coefficient using theslope A of the approximation line that is calculated in S103 andcorrects the acceleration data. In this embodiment, the analysis unit112 corrects the acceleration data by the following equations (2) and(3).

a′ _(X0) =a _(X0) cos(tan⁻¹ A)+a _(Y0) sin(tan⁻¹ A)  (2)

a′ _(y0) =−a _(X0) sin(tan⁻¹ A)+a _(Y0) cos(tan⁻¹ A)  (3)

In the above equations, a′_(X0) and a′_(Y0) are the x-component andy-component of the corrected acceleration data, a_(X0) and a_(Y0) arethe x-component and y-component of the acceleration data (acquired inS101) before correction, and cos(tan⁻¹ A) and sin(tan⁻¹ A) arecorrection coefficients. FIG. 15 shows a list of variables that are usedin the equation for correction described above or another equation forcorrection described later.

By the processing in S104, the acceleration data is corrected so thatthe slope of the approximation line of actual average accelerationscalculated in S103 matches the ideal slope of the approximation line ofaverage accelerations. Consequently, the displacement of a mounting axisof the acceleration sensor that occurs due to a mounting error or thelapse of time is substantially corrected.

Note that, although the case of performing the calculation of the slopeof the approximation line, the calculation of correction coefficientsand the correction by using the equations (1) to (3) is described inthis embodiment, this embodiment is not limited thereto. The analysisunit 112 may correct the acceleration data by other suitable equationsor the like as long as the function of correcting the axis of thedistribution of average accelerations is implemented.

Further, although the acceleration data is corrected in S104 in thisembodiment, the average acceleration may be corrected, for example,depending on purpose.

Further, in this embodiment, the average acceleration is calculated inS102 and the correction coefficient is calculated using the averageacceleration. However, this embodiment is not limited thereto, and thecorrection coefficient may be calculated using a given representativevalue other than the average value or using actually acquiredacceleration data as it is, for example, instead of using the averageacceleration. Note that, however, randomness of data distribution issuppressed and appropriate results are likely to be obtained in the caseof using a representative value such as the average value compared withthe case of using actually acquired acceleration data as it is. Anotheradvantage is that a user can easily understand an operation in theprocess of specifying a data valid region, checking a graph and thelike.

In this embodiment, the measurement device 110 corrects actualacceleration data by using a correction coefficient based on adifference between the distribution of average accelerations of theactually acquired acceleration data for each certain time period and thedistribution of ideal average accelerations. The measurement device 110can thereby perform correction (calibration) of acceleration data byusing only the statistical information of the acceleration data.Therefore, after mounting the acceleration sensor on a person, an animalor the like as an object under test, even when the person or the animalis in motion, it is possible to correct a mounting error of theacceleration sensor without direct operation of the acceleration sensor.Further, it is possible to continuously correct the displacement of theaxis that occurs with the lapse of time.

Second Embodiment

A second embodiment has a feature that it ends the acquisition ofacceleration data in the case where it is determined that a sufficientamount of data for performing statistical processing is obtained in theacceleration data acquisition step (S101 in the first embodiment).

Specifically, while the first embodiment performs calibration of theacceleration sensor using statistical data, the second embodimentexamines whether the statistical data is valid or not in advance.

The configuration of the measurement system 100 according to the secondembodiment is substantially the same as that of the first embodiment andthus not redundantly described.

The correction of acceleration data which is performed by themeasurement device 110 according to the second embodiment is describedhereinafter with reference to the flowchart of FIG. 4. Only theprocessing that is different from the first embodiment is mainlydescribed below.

S201: Acquire Acceleration Data and End Acquisition of Data

The input unit 111 of the measurement device 110 receives accelerationdata that is transmitted from the sensor module 120 by wireless or wiredcommunication in the same manner as in S101 according to the firstembodiment.

The input unit 111 calculates a coefficient of determination as neededfor the acquired acceleration data, and ends the acquisition ofacceleration data at the point of time when the variation of thecoefficient of determination becomes equal to or less than a specifiedvalue. The coefficient of determination is the statistic indicating thelinearity of data. The coefficient of determination is a statisticalmeasure, which is a value from 0 to 1, and it is closer to 1 as thedistribution of target data is closer to a straight line.

The coefficient of determination has characteristics that it has a largevalue when the amount of data is small and gradually converts to acertain value as the amount of data increases in the process ofaccumulating data. Therefore, the input unit 111 observes the variation(absolute value) of the coefficient of determination as needed and, whenthe variation falls below a specified value (for example, 0.001), forexample, it is possible to determine that sufficient points ofmeasurement (acceleration data) can be obtained (FIG. 5).

Note that it is more preferred to perform the above-describeddetermination by using not the coefficient of determination but thevariation in the coefficient of determination. This is because thecoefficient of determination largely varies depending on noise (abnormalvalue) and the amount of data, and an error can occur when theabove-described determination is performed using the coefficient ofdetermination. On the other hand, because the variation in thecoefficient of determination converges as the amount of data increases,it is suitable for use in the determination.

The equation (4) for calculating the coefficient of determination andthe equation (5) for calculating the variation in the coefficient ofdetermination are as follows.

$\begin{matrix}{R^{2} = {1 - {\frac{{\Sigma_{i}\left( {{a_{Y}(i)} - {{Aa}_{X}(i)}} \right)}^{2}}{{\Sigma_{i}\left( {{a_{Y}(i)} - {M\left( a_{Y} \right)}} \right)}^{2}} \cdot \frac{N - 1}{N - 2}}}} & (4) \\{{dR}^{2} = {{{R^{2}(n)} - {R^{2}\left( {n - 1} \right)}}}} & (5)\end{matrix}$

In the above equations, R² is the coefficient of determination, N is thenumber of all data, and n is the number of determinations of thecoefficient of determination.

Note that, although the case of determining the timing to end theacquisition of data based on the coefficient of determination or thevariation in the coefficient of determination is described in thisembodiment, this embodiment is not limited thereto. The input unit 111may determine whether the amount of acquired data is sufficient forstatistical processing by another appropriate indicator.

When it is determined that a sufficient amount of acceleration data isacquired, the input unit 111 transmits a signal for stopping orsuspending the acquisition or transmission of acceleration data to thesensor module 120 by wireless or wired communication. Alternatively, theinput unit 111 may simply stop or suspend the acquisition ofacceleration data.

S102 to S104:

The analysis unit 112 performs the correction of acceleration data inthe same procedure as in the first embodiment by using the accelerationdata acquired in S201.

In this embodiment, when the input unit 111 determines that a sufficientamount of data for performing statistical processing is obtained, itstops the acquisition of acceleration data. Thus, because theacquisition of data is stopped or suspended when a sufficient amount ofdata for statistical processing is obtained, the data acquisition workcan be automated. For example, it is feasible to end the processing in ashorter time than the specified time in the first embodiment when asufficient amount of data is acquired or perform the measurement for alonger time than the specified time when the amount of data is notsufficient. Further, in the case where the system is equipped with abattery as a power supply of the sensor module 120, it is possible tosuppress the consumption of the battery by measuring acceleration dataefficiently in this embodiment.

Third Embodiment

Although the example of correcting the displacement of the axis of theacceleration sensor is shown in the first embodiment, the thirdembodiment has a feature that it further corrects the displacement ofacceleration values.

In the case where an object under test on which an acceleration sensoris mounted operates without any deviation, the average value ofaccelerations output from the acceleration sensor should be a valueclose to the acceleration of gravity (1 G). However, this cannot beachieved in some cases due to reasons such as the initial position whenstarting up the acceleration sensor, an individual difference in theacceleration sensor and the like. For example, the average accelerationsper second in the x-direction and the z-direction are plotted as shownin FIG. 6, it is ideal that the average accelerations are distributedalong the circular arc which is indicated by the solid line. On theother hand, in some cases, the distribution of the actually acquiredaverage accelerations is the circular arc which is indicated by thedotted line, which is shifted in the z-direction from the idealdistribution.

To avoid this, in this embodiment, a correction value C for correctingthe displacement of the distribution shown in FIG. 7 is calculated, andthe actual acceleration data is corrected using the correction value sothat the actual distribution matches the ideal distribution. Stateddifferently, while the rotation of axis of the distribution is correctedin the first embodiment, the displacement (shift error) of the origin ofthe distribution is corrected in the third embodiment.

The configuration of the measurement system 100 according to the thirdembodiment is substantially the same as that of the first embodiment andthus not redundantly described.

The correction of acceleration data which is performed by themeasurement device 110 according to the third embodiment is describedhereinafter with reference to the flowchart of FIG. 7. Only theprocessing that is different from the first embodiment is mainlydescribed below.

S101 to S103:

The input unit 111 of the measurement device 110 acquires accelerationdata from the sensor module 120. The analysis unit 112 calculates theaverage value of accelerations for each certain time period for theacquired acceleration data. Further, the analysis unit 112 calculatesthe slope A of the line that approximates the distribution of theaverage accelerations.

S304: Calculate Correction Coefficient for Acceleration Values

The analysis unit 112 calculates a correction coefficient C forcorrecting acceleration values by the following equation (6).

C=mean(a _(Z)−√{square root over (1−a _(X) ²)})  (6)

In the above equation, a_(X) and a_(Z) are the x-component and thez-component of the actually acquired average acceleration. Note that,the correction coefficient C may be calculated using the x-component andthe z-component of the average acceleration corrected using the slope Aof the approximation line in S305, which is described later, instead ofa_(X) and a_(Z).

S305: Correct Acceleration by Correction Coefficient Based on Slope ofApproximation Line and Acceleration Values

The analysis unit 112 corrects the acceleration data by the correctioncoefficient using the slope A of the approximation line (equations (2)and (3)). After that, the calculated correction coefficient C is addedto the z-component of the acceleration data to thereby correct theacceleration data. For example, in the data set plotted in FIG. 6, thecorrection value C is calculated as −0.119. In this case, −0.119 isadded to the z-component of the acceleration data, and thereby thedistribution of the average accelerations along the dotted line isshifted to the distribution along the ideal solid line.

Note that, in this embodiment, the configuration that corrects thedisplacement of acceleration values is described as the one thatcomplements the configuration that corrects the displacement of the axisof the acceleration sensor according to the first embodiment. However,the configuration according to the third embodiment may be implementedindependent of the first embodiment. Specifically, the processing thatcorrects the displacement of acceleration values by the correction valueC for the acceleration values may be performed without performing theprocessing that corrects the displacement of the axis by the correctioncoefficient using the slope A of the approximation line.

Further, although the example that calculates the correction value C bythe equation (6) and makes correction is described in this embodiment,this embodiment is not limited thereto. The analysis unit 112 maycorrect the acceleration data by another appropriate expression or thelike as long as the function of correcting the center of thedistribution of average accelerations is implemented.

Further, although the acceleration data is corrected in this embodiment,the average acceleration may be corrected, for example, depending onpurpose.

Further, although the correction coefficient is calculated using theaverage acceleration in this embodiment, the correction coefficient maybe calculated using a given representative value other than the averagevalue or using actually acquired acceleration data as it is, forexample, instead of using the average acceleration.

In this embodiment, the measurement device 110 performs the processingof correcting the displacement of acceleration values. It is therebypossible to correct an error of acceleration data due to the initialposition when starting up the sensor, an individual difference in thedevice and the like even after the sensor module 120 is mounted on anobject under test such as an animal.

Fourth Embodiment

A fourth embodiment has a feature that the accuracy of correction isimproved by taking the type of action of an object under test (forexample, a person or animal) into consideration as well in thecorrection of acceleration data described in the first embodiment. Ingeneral, it is considered that the distribution of acceleration datadiffers by action pattern (such as standing up, walking etc.). Thus, itis possible to improve the accuracy of correction by performingappropriate correction processing independently for each action pattern.

Further, although the first embodiment is based on the assumption thatthe distribution of average accelerations can be approximated by thestraight line, the fourth embodiment proposes a technique of correctionusing the displacement of the center of gravity of acceleration datawhich is applicable also to the distribution of average accelerationswhere linear approximation is not appropriate.

In the measurement system 100 according to the fourth embodiment, themeasurement device 110 needs to include an input device such as akeyboard, for example, to receive a user input. The other configurationis the same as that of the first embodiment.

The processing of the measurement device 110 according to the fourthembodiment is described hereinafter with reference to the flowchart ofFIG. 8. Only the processing that is different from the first embodimentis mainly described below.

S401: Acquire Acceleration Data and Action Pattern

The input unit 111 of the measurement device 110 receives accelerationdata that is transmitted from the sensor module 120 by wireless or wiredcommunication. Note that it is preferred that the acceleration data isgenerated by the sensor module 120 and then transmitted to themeasurement device 110 without delay.

At this time, a user observes the action of an object under test (aperson or animal) and inputs information indicating the action patternto the input unit 111 through the input device. For example, when theinput device is a keyboard, keys corresponding to specified actionpatterns are defined in advance. To be more specific, a key R and a keyW may be allocated to the actions of standing up and walking,respectively. Then, a user presses the key R when a person or animal asan object under test is standing up and presses the key W when theperson or animal is walking.

The input unit 111 receives a user input indicting the action pattern ofan object under test (a person or animal) from the input device. Theinput unit 111 then associates the acquired acceleration data with dataindicating the action pattern input at that time (information about thepressed key in the case with the keyboard described above) and storesthem into the storage device.

S402: Calculate Barycentric Coordinates for Each Action Pattern

The analysis unit 112 calculates, for each action pattern, the averagevalue of all acceleration data associated with the action pattern byreferring to the storage unit. Then, the x-direction component and they-direction component of the average values of all data are defined asthe actual barycentric coordinates of the action pattern.

S403: Calculate Displacement Angle of Barycentric Coordinates for EachAction Pattern

The analysis unit 112 calculates an angle θ between the predeterminedideal barycentric coordinates in the action pattern and the actualbarycentric coordinates through the origin. For example, for the actionpattern R, an angle θ_(R) between the ideal barycentric coordinates andthe actual barycentric coordinates can be calculated by the equation(7). It is assumed that the ideal barycentric coordinates for eachaction pattern is prestored in the storage device.

$\begin{matrix}{\theta_{R} = {\cos^{- 1}\frac{{X_{R}X_{RI}} + {Y_{R}Y_{RI}}}{\sqrt{X_{R}^{2} + Y_{R}^{2}}\sqrt{X_{RI}^{2} + Y_{RI}^{2}}}}} & (7)\end{matrix}$

It is assumed that the ideal barycentric coordinates in the actionpattern R are (X_(RI),Y_(RI)), and the actual barycentric coordinates inthe action pattern R are (X_(R),Y_(R)).

S404: Calculate Correction Coefficient and Make Correction

The analysis unit 112 calculates a correction coefficient in the actionpattern R by using the displacement angle θ_(R) of the barycentriccoordinates calculated in S403 and corrects the acceleration data forthe action pattern R. In this embodiment, the analysis unit 112 correctsthe acceleration data by the following equations (8) and (9).

a′ _(X0) =a _(X0) cos θ_(R) +a _(Y0) sin θ_(R)  (8)

a′ _(Y0) =−a _(X0) sin θ_(R) +a _(Y0) cos θ_(R)  (9)

In the above equations, a′_(X0) and a′_(Y0) are the x-component and they-component of the corrected acceleration data for the action pattern R,a_(X) and a_(Y) are the x-component and y-component of the accelerationdata (acquired in S401) before correction for the action pattern R, andcos θ_(R) and a_(Y) sin θ_(R) are correction coefficients for the actionpattern R.

The analysis unit 112 performs the processing steps from S402 to S404for every action pattern.

Note that, although the correction coefficient is calculated based onthe displacement of the center of gravity in the two directions x and yin this embodiment, the correction coefficient may be calculated usingthe center of gravity in any two directions other than the above.Further, the correction coefficient may be calculated based on thedisplacement of the center of gravity in the three directions x, y andx. In the case of using the center of gravity in the three directions,the accuracy is improved but the amount of calculation increasescompared with the case of using the center of gravity in the twodirections.

Further, although the acceleration data is corrected in this embodiment,the average acceleration may be corrected, for example, depending onpurpose.

Further, although the correction coefficient is calculated using theaverage value (barycentric coordinates) of acceleration data in thisembodiment, the correction coefficient may be calculated using a givenrepresentative value other than the average value, for example, insteadof using the average acceleration.

In this embodiment, the input unit 111 associates acceleration data withan action pattern, and the analysis unit 112 corrects the accelerationdata using the displacement angle of the barycentric coordinates foreach action pattern. It is thereby possible to make appropriatecorrection even when it is not appropriate that the distribution ofacceleration data is approximated by a straight line. Particularly,because this example makes correction not on the statisticaldistribution of all data but by limiting the data to be processed toeach action pattern, the accuracy increases.

Fifth Embodiment

A fifth embodiment has a feature that it performs correction by analternative way in the case where linear approximation is notappropriate in the correction of acceleration data described in thefirst embodiment. Further, in comparison with the fourth embodiment,while correction according to the center of gravity for each actionpattern is performed in the fourth embodiment, correction according tothe center of gravity of all acceleration data is performed in the fifthembodiment.

The configuration of the measurement system 100 according to the fifthembodiment is substantially the same as that of the first embodiment andthus not redundantly described.

The processing of the measurement device 110 according to the fifthembodiment is described hereinafter with reference to the flowchart ofFIG. 9. Only the processing that is different from the first embodimentis mainly described below.

S501: Acquire Acceleration Data

The input unit 111 of the measurement device 110 receives accelerationdata that is transmitted from the sensor module 120 by wireless or wiredcommunication.

S502: Determine Correction Method by Coefficient of Determination

The input unit 111 calculates a coefficient of determination by theabove-described equation (4). When the coefficient of determination isless than a specified value (for example, 0.3), the input unit 111determines that it is not appropriate to perform linear approximation ofaverage accelerations, and performs the correction by the methoddescribed later. On the other hand, when the coefficient ofdetermination is equal to or more than the specified value, the inputunit 111 determines that it is possible to perform linear approximationof average accelerations, and performs the correction by the methoddescribed in the first embodiment, for example.

S503: Calculate Average Value of Acceleration Data

The analysis unit 112 calculates the average value of components in anarbitrary direction (which is the y-direction in this example) for allacceleration data.

S504: Calculate Average Value after Rotating Acceleration Data

The analysis unit 112 rotates all acceleration data by the angle θ withrespect to the origin on the x-y plane, and then calculates the averagevalue of the y-direction component of the acceleration data again in thesame manner as in S503. The y-direction component a′_(Y0) when theacceleration data is rotated by the angle θ can be calculated by thefollowing equation (10).

a′ _(Y0) =−a _(X0) sin θ+a _(Y0) cos θ  (10)

The analysis unit 112 calculates the average value repeatedly bychanging the angle θ with an increment of 10 degrees from −90 to +90degrees, for example.

S505: Determine Displacement Angle

The analysis unit 112 compares the predetermined ideal average valueMa_(YI)) (for example, 0) with a series of average values M(a′_(Y0))calculated in S504 and specifies the angle θ at which the absolute valueof the difference |M(a′_(Y0))−M(a_(YI))| is the smallest. It is assumedthat the ideal average value is prestored in the storage device.

FIGS. 10 and 11 show the concept of this processing. The accelerationdata plotted on the x-y plane is rotated by the angle θ with respect tothe origin (FIG. 10). Then, the value of |M(a′_(Y0))−M(a_(YI))| changeswith a change in the value of the angle θ, and it becomes the smallestat a certain value of θ (FIG. 11). In this embodiment, the value of θ atthis time is regarded as the angle at which the displacement between theactual acceleration data and the ideal value is the smallest, and thecorrection using that value of θ is performed.

S506: Calculate Correction Coefficient and Make Correction

The analysis unit 112 calculates the correction coefficient using thedisplacement angle θ calculated in S505 and corrects the accelerationdata. The correction of the acceleration data using the displacementangle can be made in the same manner as in the fourth embodiment, whichis, by replacing θ_(R) in the equations (8) and (9) with θ.

Note that, although the example that the input unit 111 performs thebranching using the coefficient of determination (S502) is described inthis embodiment, the correction after S503 may be performed directlywithout performing the branching.

Note that, although the correction coefficient is calculated based onthe average value in the y-direction in this embodiment, the correctioncoefficient may be calculated using the average value in any onedirection different from the above. Further, the correction coefficientmay be calculated in the same manner based on the center of gravity inany two or three directions. In the case of using the center of gravityin two or three directions, the accuracy is improved but the amount ofcalculation increases compared with the case of using the average valuein one direction.

Further, although the acceleration data is corrected in this embodiment,the average acceleration may be corrected, for example, depending onpurpose.

Further, although the correction coefficient is calculated using theaverage value of acceleration data in an arbitrary direction in thisembodiment, the correction coefficient may be calculated using a givenrepresentative value other than the average value, for example, insteadof using the average acceleration.

In this embodiment, the analysis unit 112 performs correction accordingto the displacement of the average values of all acceleration data. Itis thereby possible to appropriately correct the acceleration data evenwhen linear approximation of the acceleration data is not appropriate.

Sixth Embodiment

A sixth embodiment has a feature that the correction of accelerationdata described in the first embodiment is performed at specified timeintervals so that a change in the displacement of the axis can beobserved. For example, in the case where the shape of an object undertest changes with time or the case where the mounting direction orposition of the acceleration sensor can change constantly with time, itis possible to generate time-series data indicating such a change (FIG.12).

The configuration of the measurement system 100 according to the sixthembodiment is substantially the same as that of the first embodiment andthus not redundantly described.

The processing of the measurement device 110 according to the sixthembodiment is described hereinafter with reference to the flowchart ofFIG. 13. Only the processing that is different from the first embodimentis mainly described below.

S101 to S104:

The input unit 111 of the measurement device 110 receives accelerationdata that is transmitted from the sensor module 120 by wireless or wiredcommunication. The analysis unit 112 calculates average acceleration foreach certain time period and calculates the slope of a line thatapproximates the distribution of average accelerations. It thencalculates a correction coefficient using the slope of the approximationline and corrects the average acceleration.

S605: Set Sensor to Standby

The analysis unit 112 stores the calculated slope of the approximationline, which is the displacement of the axis, into the storage device ofthe measurement device 110.

The input unit 111 transmits a standby instruction to the sensor module120. The sensor module 120 receives the standby instruction and thenstops the acquisition or transmission of acceleration data andtransitions to standby mode.

S606: Start Up Sensor and Perform Repetition Processing

When a specified period of time has passed, the input unit 111 transmitsa startup instruction to the sensor module 120. The sensor module 120receives the startup instruction and then resumes the acquisition ortransmission of acceleration data and performs a series of processingsteps from S101 to S605 again. For example, when the specified period oftime is 24 hours, the input unit 111 starts up the sensor module 120every 24 hours and starts a process to calculate the axis displacement.

Further, the input unit 111 repeatedly performs the processing in S606until reaching a specified number of times. For example, when thespecified number of times is seven, the input unit 111 calculates theaxis displacement each day for one week.

In this embodiment, the input unit 111 starts up the processing ofcalculating the axis displacement repeatedly every specified time, andit is thereby possible to generate a group of data indicating a changein the axis displacement over time. Thus, even when the fixation of theacceleration sensor to an object under test is not strong enough and theaxis is likely to be displaced at all times, it is possible to correctthe displacement of the axis each time. Further, when a part (forexample, an affected part) of the body of a person or animal changesover time or a part (for example, a joint) of a structure such as abridge or tunnel changes over time, the change can be observed by fixingthe acceleration sensor to that part.

Seventh Embodiment

The measurement system 100 according to a seventh embodiment has afeature that a plurality of sensor modules 120 are connected to onemeasurement device 110 (FIG. 14).

One sensor module 120 may be mounted on each of a plurality of objectsunder test, or a plurality of sensor modules 120 may be mounted on oneobject under test. For example, by mounting a plurality of sensormodules 120 in various parts of one person or animal, it is possible todetect the action of the person or animal in more detail.

The measurement device 110 communicates with a plurality of sensormodules 120 by time division, for example. Typically, the measurementdevice 110 corrects the acceleration data acquired from the respectivesensor modules 120 independently of one another for each sensor module120. Note that, when it is desirable to acquire a large amount ofacceleration data by regarding a plurality of persons or animal as thesame individual for the sake of classifying them by gender, age, weightand the like, for example, the correction may be performed by treatingthe acceleration data acquired from the plurality of sensor modules 120as one set.

Other Embodiments

It should be noted that this embodiment is not limited to theabove-described embodiments and may be varied in many ways within thescope of the present invention. For example, the configurationsaccording to the second to sixth embodiments may be combined withanother embodiment, not only the first embodiment.

Further, although the example that mounts the acceleration sensor mainlyon a person or animal is described in the above embodiments, theacceleration sensor may be mounted on any object under test as a matterof course. For example, by mounting the acceleration sensor on astructure such as a bridge or tunnel and measuring a change inacceleration over time, it is possible to predict the aging.

Further, in the above embodiments, the example that uses the three-axisacceleration sensor as the sensor module 120 is mainly described.However, those embodiments may be applied to a single-axis accelerationsensor. For example, in the case of mounting a single-axis accelerationsensor to each of the head, back and leg of an animal as an object undertest, the measurement device 110 can process the acquired three piecesof acceleration data in the same way as the three-axis accelerationdata.

Eighth Embodiment

The configuration of a phase correction device 150 according to aneighth embodiment is described hereinafter with reference to FIG. 16.

The phase correction device 150 receives acceleration data that isoutput from the acceleration sensor (not shown) and performs phasecorrection of the acceleration data. The phase correction device 150 istypically an information processing device such as a PC (personalcomputer), a server computer or a microcontroller, and it is implementedby a CPU (Central Processing Unit), a storage device such as a volatileor nonvolatile memory, an input/output device and the like. The phasecorrection device 150 performs specified processing based on a programstored in the storage device and thereby logically implements a featurequantity estimation unit 160, a phase estimation unit 170 and a phasecorrection unit 180, which are described later.

Note that the acceleration sensor is mounted on an object, measuresgenerated acceleration, and outputs acceleration data indicating ameasured value. It is assumed that the acceleration data containaccelerations in three-axis (x-axis, y-axis and z-axis) directions.

The feature quantity estimation unit 160 receives the acceleration dataand performs statistical processing. The feature quantity estimationunit 160 includes a standard deviation calculation unit 162 and anaverage calculation unit 164. The standard deviation calculation unit162 calculates a standard deviation value of the acceleration data foreach axis. The average calculation unit 164 calculates an average valueof the acceleration data for each axis.

The phase estimation unit 170 performs bias correction and phaseestimation of a specified axis by using the average values calculated bythe average calculation unit 164 according to the standard deviationvalue that is calculated by the standard deviation calculation unit 162.

The phase correction unit 180 performs phase correction on the averagevalues calculated by the average calculation unit 164 according to thephase estimation result by the phase estimation unit 170 and outputs aprocessing result.

The overview of a phase correction method of the phase correction device150 according to the eighth embodiment is described hereinafter. Thismethod performs the following procedure to make phase correction of theacceleration sensor.

First, the displacement of an angle from acceleration data that isobtained in an ideal placement state is estimated from the phase ofactually acquired acceleration data, which is phase data that containsan error caused by the placement state of the acceleration sensor to anobject. The ideal placement state is the state where the accelerationsensor is placed at a placement position where the characteristics ofthe action of an object are likely to emerge with the axes of theacceleration sensor coinciding with the directions that are likely toobserve the characteristics of the action of the object, for example.

When estimating the phase of actually acquired acceleration data, it isdesired to use the acceleration data when an object is in the state ofrest. Thus, this embodiment extracts only the acceleration data duringthe period when the standard deviation of the acceleration data is lessthan a specified threshold and estimates the phase of the accelerationdata.

Next, the phase of the actually obtained acceleration data is corrected.Specifically, the actually obtained acceleration data is corrected to avalue that could have been obtained with the ideal placement state ofthe acceleration sensor.

This embodiment calculates the average value of acceleration data thatis acquired during each certain time period, not the acquiredacceleration data itself, and performs phase correction on the averagevalue. By using a representative value of acceleration data in thismanner, it is possible to suppress a load of the phase correction.Further, because the average value of acceleration data is a featurequantity that is suitable for action identification of an object, it ispossible to use the average value after phase correction as it is foraction identification.

The reason that the average value of acceleration data is suitable as afeature quantity for action identification is as follows. The presentinventor has found that the distribution of the average values ofacceleration data of each certain time period is deviated by the type ofaction (for example, movement, a change in posture etc.) of an object.Specifically, when the average values of the values of the x-axis andthe y-axis of acceleration data are calculated every certain time periodand those average values are plotted on the x-y axis, for example, thereis a tendency that a group of each action type is formed in a differentregion on the coordinate plane.

The operation of the phase correction device 150 according to the eighthembodiment is described hereinafter with reference to the flowchart ofFIG. 17.

S10:

Acceleration data is input from the acceleration sensor (not shown) tothe standard deviation calculation unit 162 of the feature quantityestimation unit 160. Generally, the acceleration data is continuouslyinput from the start to the end of measurement.

In this embodiment, the phase correction device 150 receives only thevalue aX in the x-direction and the value aY in the y-direction amongthe acceleration data. According to the knowledge of the presentinventor, high accuracy can be achieved by using only aX and aY in anyof the determination of the resting state and the action identification.Note that this embodiment is not limited thereto, and the sameprocessing may be performed using values in other arbitrary two-axis orthree-axis directions.

The standard deviation calculation unit 162 calculates, at every certaintime interval (for example, one second), the standard deviation of agroup of acceleration data input during that interval. The standarddeviation calculation unit 162 repeats the calculation of the standarddeviation.

S11:

Acceleration data is input from the acceleration sensor to the averagecalculation unit 164 of the feature quantity estimation unit 160, inparallel with the standard deviation calculation unit 162. Then, atevery certain time interval (for example, one second), the averagecalculation unit 164 calculates the average value of acceleration datainput during that interval. The average calculation unit 164 repeatedlyperforms the calculation of average value and outputs a result of theprocessing to the phase estimation unit 170 and the phase correctionunit 180 each time.

S12:

The standard deviation calculation unit 162 determines whether thestandard deviation calculated in S10 is smaller than a predeterminedthreshold. For example, when the standard deviation of a value in thex-axis direction is σ_(x), the standard deviation of a value in they-axis direction is σ_(y), the threshold for a value in the x-axisdirection is TH1, and the threshold for a value in the y-axis directionis TH2, the above-described determination can be made by the followingequation (11). When it is determined as “YES” in the equation (11), thephase estimation unit 170 performs the processing of S13.

if (σ_(X) <TH1&σ_(y) <TH2) then YES  (11)

S13:

The average value calculated by the average calculation unit 164 isinput to the phase estimation unit 170 at certain time intervals.Further, a notification indicating that the standard deviation issmaller than a threshold is input as needed from the standard deviationcalculation unit 162. When this notification is input, the phaseestimation unit 170 stores the average value that is input at the sametiming into a register. The phase estimation unit 170 repeats thisstorage process until a specified number of average values areaccumulated in the register.

Although the number of times of accumulating average values may be setarbitrarily, the accuracy of phase estimation increases as the number oftimes of accumulation increases.

S14:

The phase estimation unit 170 performs bias correction and phaseestimation on the specified number of average values accumulated in theregister.

To illustrate this processing, FIG. 18 shows the example in which theaverage values stored in S13 are plotted in the x-y coordinates. Thedistance between the intersection between the approximation line ofthose average values and the y-axis and the origin is referred to asbias. The bias correction is the operation that makes the intersectionand the origin coincide with each other. Further, the angle φ′ betweenthe approximation line and the line y=bias is referred to as phase. Thephase correction is the operation that corrects the phase φ′ to 0.

In this embodiment, the bias is calculated in a simplified manner by thefollowing way. The phase estimation unit 170 extracts average values inproximity to the y-axis, and calculates the average of the y-coordinatesof those average values as the bias. This calculation is represented bythe following equation (12). In this equation, TH3 is a threshold thatdefines the proximity range to the y-axis, ρ_(x)(i) is the x-coordinateof each average value, and μ_(y)(i) is the y-coordinate of each averagevalue. Further, μ_(y2)(i) is the y-coordinate of each average valueafter bias correction.

$\begin{matrix}{{{if}\mspace{14mu} \left( {{{abs}\left( {\mu_{x}(i)} \right)} < {{TH}\; 3}} \right)}{\mu_{ysum} = {\sum{\mu_{y}(i)}}}{{{bias} = \frac{\mu_{ysum}}{N}},{{\mu_{y\; 2}(i)} = {{\mu_{y}(i)} - {bias}}}}} & (12)\end{matrix}$

Further, in this embodiment, the phase is calculated in a simplifiedmanner by the following way. First, the phase estimation unit 170calculates the average coordinates of the average values which have beenbias-corrected and whose x-coordinates are on the negative side. Thiscalculation is represented by the following equation (13). Then, thephase estimation unit 170 calculates the angle φ₁ between the averagecoordinates and the origin. This calculation is represented by thefollowing equation (14).

$\begin{matrix}{{{if}\left( {{\mu_{x}(i)} < 0} \right)}{{\mu_{{xave}\; 1} = \frac{\sum{\mu_{x}(i)}}{M\; 1}},{\mu_{y\; 2\; {ave}\; 1} = \frac{\sum{\mu_{y\; 2}(i)}}{M\; 1}}}} & (13) \\{\varphi_{1} = {a\mspace{14mu} \tan \; 2\left( {\mu_{y\; 2\; {ave}\; 1},\mu_{{xave}\; 1}} \right)}} & (14)\end{matrix}$

Likewise, the phase estimation unit 170 calculates the averagecoordinates of the average values which have been bias-corrected andwhose x-coordinates are on the positive side. This calculation isrepresented by the following equation (15). Then, the phase estimationunit 170 calculates the angle φ₂ between the average coordinates and theorigin. This calculation is represented by the following equation (16).

$\begin{matrix}{{{if}\left( {{\mu_{x}(i)} > 0} \right)}{{\mu_{{xave}\; 2} = \frac{\sum{\mu_{x}(i)}}{M\; 2}},{\mu_{y\; 2\; {ave}\; 2} = \frac{\sum{\mu_{y\; 2}(i)}}{M\; 2}}}} & (15) \\{\varphi_{2} = {a\mspace{14mu} \tan \; 2\left( {\mu_{y\; 2\; {ave}\; 2},\mu_{{xave}\; 2}} \right)}} & (16)\end{matrix}$

Finally, the phase estimation unit 170 averages the angles φ₁ and φ₂ andthereby obtains the phase φ′. This calculation is represented by thefollowing equation (17).

$\begin{matrix}{\varphi^{\prime} = \frac{\left( {\pi - \varphi_{1}} \right) + \left( {0 - \varphi_{2}} \right)}{2}} & (17)\end{matrix}$

S15:

The phase correction unit 180 performs phase correction of each averagevalue after the bias correction by using the phase φ′ that is calculatedin S14. This calculation is represented by the following equation (18).Further, FIG. 19 shows the example in which the average values after thephase correction in S15 are plotted in the x-y coordinates.

μ′_(x)(i)=μ_(x)(i)·cos(φ′)−μ_(y2)(i)·sin(φ′)

μ′_(y)(i)=μ_(x)(i)·sin(φ′)−μ_(y2)(i)·cos(φ′)  (18)

Preferably, the phase correction device 150 repeatedly performs a seriesof processing steps from S10 to S15 periodically, for example, for ameasurement period of acceleration data.

According to this embodiment, the phase estimation unit 170 estimatesthe displacement of the mounting position and direction of theacceleration sensor as a phase. Further, the phase correction unit 180corrects the estimated phase. It is thereby possible to correct thedisplacement of the mounting position and direction of the accelerationsensor that can occur due to individual differences or the lapse of timeand obtains the acceleration data that is always in substantially thesame phase only by the statistical processing of acceleration datawithout performing calibration of the acceleration sensor. Thus, if athreshold or a machine learning parameter for action identification iscalculated once, the same threshold or machine learning parameter can beused in common even for the acceleration data obtained from anotherindividual or the acceleration data where the phase displacement occursdue to long-term measurements.

Further, in this embodiment, the phase estimation unit 170 performsestimation of a bias and estimation of a phase in simplified ways asdescribed above. Further, the phase correction process is performedusing the average value of acceleration data for each certain timeperiod. It is thereby possible to suppress the processing load andachieve the high-speed phase correction.

Ninth Embodiment

A ninth embodiment relates to an action identification device 200 thatincludes a phase correction device 150. First, the configuration of thephase correction device 150 according to the ninth embodiment isdescribed hereinafter with reference to FIG. 22.

The action identification device 200 receives acceleration data that isoutput from the acceleration sensor and performs processing to identifythe action of an object by using the phase-corrected acceleration data.The action identification device 200 is typically an informationprocessing device such as a PC (personal computer), a server computer ora microcontroller, and it logically implements a phase correction device150, an identification learning unit 210, an identification processingunit 220, a parameter storing unit 230, a second identificationprocessing unit 240, a threshold storing unit 250, a classification unit260, and an identification determination unit 270 by executing specifiedprocessing based on a program stored in the storage device.

Note that, although the action identification device 200 is implementedto be integral with the phase correction device 150 by way ofillustration in this embodiment, the action identification device 200and the phase correction device 150 may be separate devices. In thiscase, the action identification device 200 receives the accelerationdata that has been phase-corrected by the phase correction device 150and performs action identification.

The phase correction device 150 receives the acceleration data that isgenerated by the acceleration sensor and performs the phase correctionby the procedure described in the eighth embodiment.

The identification learning unit 210 performs learning using thephase-corrected acceleration data and thereby produces theidentification processing unit 220 as a discriminator that identifiesaction types. Typically, the identification learning unit 210 and theidentification processing unit 220 are implemented by SVM (SupportVector Machine). Note that the identification learning unit 210 and theidentification processing unit 220 can be substituted by other knownlearning and identification mechanism as a matter of course.

The parameter storing unit 230 is a storage region that stores aparameter for the identification processing unit 220 (which is the SVMin this embodiment) to operate. Note that a specific parameter that canbe used for the operation of a discriminator such as the SVM is alreadyknown and thus not redundantly described.

The classification unit 260 classifies the average values of theacceleration data into action types (classes) identified by theidentification processing unit 220 and stores them.

The second identification processing unit 240 performs processing toidentify the action types of acceleration data by a different way fromthe identification learning unit 210 and the identification processingunit 220. In this embodiment, the second identification processing unit240 extracts the acceleration data that is related to a specific actiontype by using time-axis cross-correlation properties.

The threshold storing unit 250 stores a threshold that is used foraction identification that is performed by the second identificationprocessing unit 240. Specifically, the second identification processingunit 240 performs action identification by using the threshold stored inthe threshold storing unit 250.

The identification determination unit 270 puts together actionidentification results that are stored in the classification unit 260and action identification results that are output from the secondidentification processing unit 240 and thereby performs highly accurateaction identification and outputs an identification result.

The operation of the action identification device 200 is describedhereinafter with reference to the flowchart of FIG. 23.

S30:

The phase correction device 150 receives acceleration data from theacceleration sensor and performs phase estimation and phase correctionon the average values of the acceleration data. In this embodiment, itperforms phase correction on the average values for each certain timeperiod of all acceleration data input from the acceleration sensor.

In FIG. 20, the average values of all acceleration data before phasecorrection by the phase correction device 150 are plotted in the x-ycoordinates. As described also in the eighth embodiment, a bias and aphase occur also in FIG. 20. Those bias and phase differ by eachindividual of an object and further indicate different values dependingon the position where the acceleration sensor is placed. Therefore, evenwhen an action identification parameter is calculated from theacceleration data acquired in a certain individual, this parametercannot be applied to the action identification by the acceleration dataacquired in another individual. For example, the sample of FIG. 20includes the acceleration data belonging to three action types: classesA, B and C. When discriminating among those three classes, it is neededto calculate parameters for discriminating between the classes A and Band between classes B and C, respectively. Further, the specification ofthose parameters needs to be performed for each individual and eachmeasurement.

On the other hand, in FIG. 21, the average values of all accelerationdata after phase correction by the phase correction device 150 areplotted in the x-y coordinates. If an action identification parameter iscalculated once using those phase-corrected acceleration data, it ispossible to apply this parameter to the action identification even whena phase displacement occurs in each individual or each identification.

The phase correction device 150 outputs the average values ofacceleration data after phase correction to the identification learningunit 210 or the identification processing unit 220. When the subsequentprocessing is creation of a discriminator, which is, learning (S31), thephase correction device 150 outputs them to the identification learningunit 210. On the other hand, when the discriminator is already createdand the subsequent processing is action identification (S36), the phasecorrection device 150 outputs them to the identification processing unit220.

<Learning Processing> S31:

The identification learning unit 210 inputs the average values ofacceleration data after phase correction to the SVM. The SVM therebygenerates a parameter indicating the most appropriate boundary forclassifying the input average values into action types.

The SVM is a discriminator that discriminates between two classes. Thus,in the case of discriminating among three or more classes of actiontypes, it is necessary to combine a plurality of discriminators thatdiscriminate between two classes. For example, in order to discriminateamong the three classes A, B and C, a discriminator 1 that discriminatesbetween the classes A and B and a discriminator 2 that discriminatesbetween the classes B and C are created. Thus, the creation of adiscriminator is performed two times.

S32:

The identification learning unit 210 stores the parameter for thediscriminator created in S31 into the parameter storing unit 230. Notethat, in the case where a plurality of discriminators are created inS31, parameters for the respective discriminators are stored.

<Action Identification> S33:

The second identification processing unit 240 receives acceleration datafrom the acceleration sensor and extracts a specified feature quantitythat can be used for improving identification accuracy. Further, itperforms action identification by using the feature quantity.

For example, when there is a feature quantity capable of specifying onlythe fact that acceleration data belongs to the class B, the secondidentification processing unit 240 can extract it. In this case, if thelogical AND is carried out between identification results of theidentification processing unit 220 and identification results of thesecond identification processing unit 240 in the subsequent processing,the action identification with higher accuracy can be achieved for theclass B compared with the case of using either one identificationprocessing unit only.

Accordingly, the feature quantity that is extracted in this step ispreferably different from the one used by the identification processingunit 220. For example, the present inventor has found that, when anobject is walking, the time-axis cross-correlation properties of theacceleration data become characteristic. To be specific, periodic datahaving the characteristics of the walking operation (for example, SINwaveform with several Hz) is first generated arbitrarily. Next, thesecond identification processing unit 240 calculates a cross-correlationvalue between the periodic data and the acceleration data. When thecross-correlation value is larger than a specified threshold, the secondidentification processing unit 240 determines that those accelerationdata indicate the walking operation. Note that, in order to increase thedetection accuracy, an action may be detected in the case where thenumber of times the cross-correlation value exceeds the threshold isequal to or more than a specified number.

S34:

The second identification processing unit 240 stores a thresholdrequired for identifying an action type by the feature quantityextracted in S33 into the threshold storing unit 250.

S35:

The second identification processing unit 240 performs actionidentification by using the feature quantity extracted in S33 and thethreshold stored in the threshold storing unit 250.

S36:

The identification processing unit 220 inputs the average value ofacceleration data after phase correction into the SVM. The SVM therebyidentifies an action type or class to which the input average valuebelongs and outputs the result to the classification unit 260.

Note that, in the case of discriminating among three or more classes ofaction types, it is necessary to combine a plurality of discriminatorsthat discriminate between two classes. For example, in order todiscriminate among the three classes A, B and C, action identificationis performed two times using a discriminator 1 that discriminatesbetween the classes A and B and a discriminator 2 that discriminatesbetween the classes B and C.

S37:

The classification unit 260 holds classification results in S36.

S38:

The identification determination unit 270 puts together actionidentification results held by the classification unit 260 and actionidentification results by the second identification processing unit 240and thereby perform final action identification.

For example, it is assumed that the classification unit 260 holds whichof the three classes A, B and C is identified in S36, and the secondidentification processing unit 240 holds whether the class B isidentified or not in S35. In this case, the identification determinationunit 270 carries out the logical AND between identification results heldin the classification unit 260 and identification results held in thesecond identification processing unit 240. Then, only when both of theclassification unit 260 and the second identification processing unit240 have the determination result indicating the class B, theidentification determination unit 270 determines the finalidentification result as the class B. Therefore, for the class B, highlyaccurate action identification can be achieved compared with the case ofusing either one identifier only. In other words, the identificationdetermination unit 270 can filter the identification result of the SVMusing the identification result of the second identification processingunit 240.

Note that, for the class for which action identification is notperformed by the second identification processing unit 240, theidentification result that is stored in the classification unit 260 maybe output as it is.

In this embodiment, the phase correction device 150 performs phasecorrection of the average values of acceleration data, and theidentification learning unit 210 and the identification processing unit220 perform action identification by using the average values after thephase correction. Thus, a class identification parameter that iscalculated once can be applied to another individual and measurement,and it is thereby possible to improve the efficiency of actionidentification.

Further, in this embodiment, the identification determination unit 270puts together the identification results identified by theidentification processing unit 220 and the identification results by thesecond identification processing unit 240 and thereby outputs the finalidentification result. It is thereby possible to achieve highly accurateaction identification.

Tenth Embodiment

A tenth embodiment has a feature that the efficiency of learning andidentification are improved in the identification learning unit 210 andthe identification processing unit 220 according to the ninthembodiment.

The overview of the processing according to the tenth embodiment isdescribed with reference to FIGS. 21 and 24. First, consider the casewhere the average values of acceleration data in the distribution shownin FIG. 21 are input to the identification learning unit 210 and theidentification processing unit 220 to perform learning andidentification. In this example, the distribution range of the class Ais large, and therefore the number of SV (support vectors) that definesthe range of the class is likely to be large. In this case, the learningaccuracy can be degraded depending on the number of samples to belearned.

Thus, in this embodiment, processing of producing an absolute value isadded for a value on the vertical axis side (a value in the y-axisdirection) among the average values of acceleration data. Thedistribution of the average values of acceleration data after suchprocessing is shown in FIG. 24. In FIG. 24, the sample distributionrange is reduced to about half compared with FIG. 21. Accordingly, thenumber of SV for defining the range of a class decreases. In addition,the number of samples existing in the class distribution range, which isthe density of samples, increases to about double.

The action identification device 200 according to the tenth embodimenthas the feature in which the phase correction device 150 outputs averagevalues of acceleration data after phase correction. The otherconfiguration is the same as in the ninth embodiment and thus notredundantly described.

The operation of the action identification device 200 in the tenthembodiment is described hereinafter with reference back to the flowchartof FIG. 23. Note that only a different operation from the ninthembodiment is mainly described below, and the other operation is notredundantly described.

S30:

The phase correction device 150 receives acceleration data from theacceleration sensor and performs phase estimation and phase correctionon the average value of the acceleration data. Further, for the averagevalue of the acceleration data after phase correction, the phasecorrection unit 180 of the phase correction device 150 updates a valuein a specified axis direction to the absolute value of that value.

The specified axis can be determined based on the expected distributionstatus of average values of acceleration data. For example, when theaverage values of acceleration data are distributed to be substantiallysymmetrical to the x-axis as shown in FIG. 21, the advantageous effectsof this embodiment can be exerted most effectively by producing theabsolute value for the value in the y-axis direction.

The phase correction device 150 outputs the average values ofacceleration data after the update to the identification learning unit210 or the identification processing unit 220. After that, theprocessing steps of S31 to S38 are performed in the same manner as inthe second example. Note that it is not necessary to use theabove-described average values in the identification in the processingsteps S33 to S35.

In this embodiment, the phase correction unit 180 produces the absolutevalue of a value in a specified axis direction for the average values ofacceleration data. The number of samples in the distribution range of aclass thereby increases to improve the learning accuracy. Further,because the length of an identification support vector becomes shorter,it is possible to reduce the amount of calculation for identification.

Eleventh Embodiment

An eleventh embodiment describes one example of an action identificationsystem 300 that includes the action identification device 200. FIG. 25shows a configuration example of the action identification system 300.

A transmitting unit 310 is used by being mounted on an object undertest, and measures generated acceleration and outputs acceleration dataindicating a measured value. The transmitting unit 310 includes anacceleration sensor 311, an MCU (Micro Control Unit) 313 that generatesacceleration data from an output signal of the acceleration sensor andoutputs it, and an RF unit 312 that modulates the acceleration dataoutput from the MCU 313 and transmits it by wireless way. The RF unit312 is preferably a low-power FSK modulation device such as a Bluetooth(registered trademark). Note that another modulation method (SubGHz,Zigbee (registered trademark), WiFi etc.) can be used in the RF unit 312as a matter of course.

A receiving unit 320 receives the acceleration data that is output fromthe transmitting unit 310, performs statistical processing on theacceleration data and thereby modifies a mounting error of theacceleration sensor. The receiving unit 320 is typically an informationprocessing device such as a PC (personal computer), a server computer ora microcontroller. The receiving unit 320 includes an RF unit 322 thatreceives and demodulates the acceleration data that is transmitted bythe RF unit 312, an MCU 323 that processes the acceleration data that isdemodulated by the RF unit 322, and an action identification device 200that receives the acceleration data that is output from the MCU 323 andperforms action identification processing.

The action identification system 300 may further include a display unit330 that displays an identification result by the action identificationdevice 200 in a viewable manner.

Twelfth Embodiment

A twelfth embodiment describes one example of a microcontroller 400 thatincludes an action identification device 200. FIG. 26 shows aconfiguration example of the microcontroller 400.

The microcontroller 400 includes an action identification device 200, aSEL 410, and a register setting unit 420.

The SEL 410 is a selector for selecting external input data anddetermining the transfer destination of the input data.

The register setting unit 420 performs control to switch the actionidentification device 200 to learning mode or identification modeaccording to external control. For example, it is the learning mode whena value of the register is set to 0, and it is the identification modewhen a value of the register is set to 1.

When the register is set to 0, which is the learning mode, the registersetting unit 420 controls the SEL 410 to select learning data as inputdata. Further, the register setting unit 420 sets the identificationlearning unit 210 to the operating state and sets the identificationprocessing unit 220 to the suspended state.

After that, the standard deviation calculation unit 162 and the averagecalculation unit 164 of the phase correction device 150 and the secondidentification processing unit 240 start operating and calculate afeature quantity of the input data. Then, the input data that has beenphase-corrected by the phase correction device 150 is input to theidentification learning unit 210. The identification learning unit 210calculates an action identification parameter and stores it into theparameter storing unit 230. Note that the identification parameter maybe directly set from the outside through the register setting unit 420.

Concurrently, the second identification processing unit 240 stores anidentification threshold into the threshold storing unit 250. Theidentification threshold may be also directly set from the outsidethrough the register setting unit 420.

On the other hand, when the register is set to 1, which is theidentification mode, the register setting unit 420 controls the SEL 410to select identification data as input data. Further, the registersetting unit 420 sets the identification learning unit 210 to thesuspended state and sets the identification processing unit 220 to theoperating state.

After that, the standard deviation calculation unit 162 and the averagecalculation unit 164 of the phase correction device 150 and the secondidentification processing unit 240 start operating and calculate afeature quantity of the input data. Then, the input data that has beenphase-corrected by the phase correction device 150 is input to theidentification processing unit 220.

The identification processing unit 220 performs action identification byusing the identification parameter that is stored in the parameterstoring unit 230. Concurrently, the second identification processingunit 240 performs action identification by using the identificationthreshold that is stored in the threshold storing unit 250.

Finally, the classification unit 260 makes classification into classes,and the identification determination unit 270 performs the final actionidentification and then ends the process.

Other Embodiments

Note that embodiments are not limited to the above-described embodimentsand may be varied in many ways appropriately within the scope of thepresent invention. For example, the example that estimates a phase usinga group of acceleration data whose standard deviation value is equal toor less than a threshold and performs phase correction on the same groupof acceleration data is described in the eighth embodiment. However, itis feasible to perform phase correction on a group of acceleration dataincluding data whose standard deviation value is not equal to or lessthan a threshold also using the above-described phase estimation result.

Further, the example in which the identification determination unit 270performs highly accurate action identification by using anidentification result of the second identification processing unit 240is described in the ninth embodiment. However, the action identificationdevice 200 does not necessarily have such a configuration, and it maysimply output an identification result to be stored into theclassification unit 260.

Further, although the example that performs a series of processing stepsby using the average value of acceleration data for each certain timeperiod is described in the above embodiments, an embodiment of thepresent invention is not limited thereto, and the same processing may beperformed using another representative value. Note that, althoughacceleration data, not a representative value, can be used as it is forphase correction, learning and identification processing, this can causean increase in processing load and a decrease in the accuracy oflearning and identification.

Further, although each embodiment is implemented as a hardwareconfiguration in the description of the above embodiments, the presentinvention is not limited thereto, and a given processing unit may belogically implemented by causing a CPU (Central Processing Unit) toexecute a computer program. In this case, the computer program can bestored and provided to a computer using any type of non-transitorycomputer readable media. Non-transitory computer readable media includeany type of tangible storage media. Examples of non-transitory computerreadable media include magnetic storage media (such as floppy disks,magnetic tapes, hard disk drives, etc.), optical magnetic storage media(e.g. magneto-optical disks), CD-ROM (compact disc read only memory),CD-R (compact disc recordable), CD-R/W (compact disc rewritable), andsemiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM(erasable PROM), flash ROM, RAM (random access memory), etc.). Theprogram may be provided to a computer using any type of transitorycomputer readable media. Examples of transitory computer readable mediainclude electric signals, optical signals, and electromagnetic waves.Transitory computer readable media can provide the program to a computervia a wired communication line (e.g. electric wires, and optical fibers)or a wireless communication line.

Further, the configurations of the above-described first to twelfthembodiments can be combined as desirable by one of ordinary skill in theart

The whole or part of the embodiments disclosed above can be describedas, but not limited to, the following supplementary notes.

(Supplementary Note 1)

A measurement device comprising:

an input unit that inputs a plurality of acceleration data; and

an analysis unit that compares a distribution of a first accelerationvalue based on the acceleration data with a distribution of apredetermined second acceleration value and thereby corrects the firstacceleration value.

(Supplementary Note 2)

The measurement device according to Supplementary note 1, wherein theanalysis unit corrects the first acceleration value according to adisplacement between a slope of a straight line approximating thedistribution of the first acceleration value and a slope of a straightline approximating the distribution of the second acceleration value.

(Supplementary Note 3)

The measurement device according to Supplementary note 1 or 2, whereinthe analysis unit further corrects the first acceleration valueaccording to a displacement between a center of a circular arcapproximating the distribution of the first acceleration value and acenter of a circular arc approximating the distribution of the secondacceleration value.

(Supplementary Note 4)

The measurement device according to any one of Supplementary notes 1 to3, wherein the first acceleration value is an average value of theacceleration data for each certain time period.

(Supplementary Note 5)

The measurement device according to any one of Supplementary notes 1 to4, wherein the analysis unit stops acquisition of the acceleration dataor correction of the first acceleration value based on the variation ofa coefficient of determination for the acceleration data.

(Supplementary Note 6)

The measurement device according to any one of Supplementary notes 1 to5, wherein the analysis unit generates and displays a graph representingat least one of the distribution of the first acceleration value and thedistribution of the second acceleration value.

(Supplementary Note 7)

The measurement device according to any one of Supplementary notes 2 to6, wherein the analysis unit performs approximation of the firstacceleration value distributed in a specified data value region to thestraight line.

(Supplementary Note 8)

The measurement device according to Supplementary note 1, wherein

the input unit further receives input for classifying the accelerationdata into a plurality of groups, and

the analysis unit corrects, in each of the groups, the firstacceleration value according to a displacement between the average valueof the acceleration data classified into the group and a reference pointpredetermined for each of the groups.

(Supplementary Note 9)

The measurement device according to Supplementary note 1, wherein theanalysis unit calculates, for each of a plurality of angles θ, adifference between an average value of acceleration data obtained byrotating the acceleration data by the angle θ and a predeterminedreference value, and corrects the first acceleration value by using θwhere the difference is smallest.

(Supplementary Note 10)

The measurement device according to any one of Supplementary notes 1 to9, wherein the input unit repeatedly inputs the acceleration data atspecified time intervals.

(Supplementary Note 11)

A measurement system comprising:

a sensor module that is mounted on an object under test and outputsacceleration data; and

a measurement device that acquires the acceleration data from the sensormodule, wherein

the measurement device includes

-   -   an input unit that inputs a plurality of acceleration data; and    -   an analysis unit that compares a distribution of a first        acceleration value based on the acceleration data with a        distribution of a predetermined second acceleration value and        thereby corrects the first acceleration value.

(Supplementary Note 12)

The measurement system according to Supplementary note 11, wherein

the measurement device acquires the acceleration data from a pluralityof sensor modules, and

the analysis unit corrects the first acceleration value by using theacceleration data acquired from each of the sensor modules.

(Supplementary Note 13)

A measurement method comprising:

an input step of inputting a plurality of acceleration data; and

an analysis step of comparing a distribution of a first accelerationvalue based on the acceleration data with a distribution of apredetermined second acceleration value and thereby correcting the firstacceleration value.

(Supplementary Note 14)

A program causing a computer to execute the method according toSupplementary note 13.

While the invention has been described in terms of several embodiments,those skilled in the art will recognize that the invention can bepracticed with various modifications within the spirit and scope of theappended claims and the invention is not limited to the examplesdescribed above.

Further, the scope of the claims is not limited by the embodimentsdescribed above.

Furthermore, it is noted that, Applicant's intent is to encompassequivalents of all claim elements, even if amended later duringprosecution.

What is claimed is:
 1. A phase correction device comprising: a standarddeviation calculation unit that receives a plurality of accelerationdata and calculates a standard deviation of the plurality ofacceleration data for each specified time period; a representative valuecalculation unit that receives the plurality of acceleration data andcalculates a representative value of the acceleration data for eachspecified time period; a phase estimation unit that estimates a phase ofthe representative value in a space having a first coordinate axis and asecond coordinate axis by using the representative value when thestandard deviation is smaller than a specified threshold; and a phasecorrection unit that performs phase correction of the representativevalue by using the estimated phase.
 2. The phase correction deviceaccording to claim 1, wherein the phase estimation unit performs atleast one processing selected from: processing that calculates a firstphase based on an average of the representative values on a negativeside of the first coordinate axis, processing that calculates a secondphase based on an average of the representative values on a positiveside of the first coordinate axis, and processing that estimates thephase based on the first phase and the second phase.
 3. The phasecorrection device according to claim 2, wherein the phase estimationunit further calculates a bias based on an average of the representativevalues near the second coordinate axis, calculates the first phase basedon the bias and the average of the representative values on the negativeside of the first coordinate axis, and calculates the second phase basedon the bias and the average of the representative values on the positiveside of the first coordinate axis.
 4. The phase correction deviceaccording to claim 1, wherein the representative value is an averagevalue.
 5. The phase correction device according to claim 1, wherein thephase correction unit updates a coordinate value of the secondcoordinate axis by an absolute value of the coordinate value of thesecond coordinate axis among the representative values.
 6. An actionidentification device comprising: the phase correction device accordingto claim 1; an identification learning unit that performs machinelearning by using the representative value after phase correction by thephase correction device; and an identification processing unit thatperforms action identification by using the representative value afterphase correction by the phase correction device.
 7. The actionidentification device according to claim 6, further comprising: a secondidentification processing unit that performs action identificationwithout machine learning by using a feature quantity of the accelerationdata; and an identification determination unit that puts together actionidentification results by the identification processing unit and thesecond identification processing unit.
 8. An action identificationsystem comprising: the action identification device according to claim6; and a transmitting unit including an acceleration sensor that ismounted on an object and outputs the acceleration data.
 9. Amicrocontroller comprising: the action identification device accordingto claim 6; and a resistor setting unit that sets any one of theidentification learning unit and the identification processing unit toan operating state according to external control.
 10. A phase correctionmethod comprising: a standard deviation calculation step of receiving aplurality of acceleration data and calculating a standard deviation ofthe plurality of acceleration data for each specified time period; arepresentative value calculation step of receiving the plurality ofacceleration data and calculating a representative value of theacceleration data for each specified time period; a phase estimationstep of estimating a phase of the representative value in a space havinga first coordinate axis and a second coordinate axis by using therepresentative value when the standard deviation is smaller than aspecified threshold; and a phase correction step of performing phasecorrection of the representative value by using the estimated phase. 11.A non-transitory computer-readable recording medium storing a programcausing a computer to execute the phase correction method according toclaim 10.